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1 |
IoT architecture for continuous long term monitoring: Parkinson’s Disease case study
معماری اینترنت اشیا برای نظارت طولانی مدت مداوم: مطالعه موردی بیماری پارکینسون-2022 In recent years, technological advancements and the strengthening of the Internet of Things
concepts have led to significant improvements in the technology infrastructures for remote
monitoring. This includes telemedicine which is the ensemble of technologies and tools involved
in medical services, from consultations, to diagnosis, prescriptions, treatment and patient
monitoring, all done remotely via an Internet connection.
Developing a telemedicine framework capable of monitoring patients over a continuous long-term monitoring window may encounter various issues related to the battery life of the device or the accuracy of the retrieved data. Moreover, it is crucial to develop an IoT architecture that is adaptable to various scenarios and the ongoing changes of the application scenario under analysis. In this work, we present an IoT architecture for continuous long-term monitoring of patients. Furthermore, as a real scenario case study, we adapt our IoT architecture for Parkinson’s Disease management, building up the PDRMA (Parkinson’s disease remote monitoring architecture). Performance analysis for optimal operation with respect to temperature and daily battery life is conducted. Finally, a multi-parameter app for the continuous monitoring of Parkinson’s patients is presented. keywords: IoT | Telemedicine | Continuous long term monitoring | Parkinson’s disease | e-Health |
مقاله انگلیسی |
2 |
DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks
DQRA: عامل مسیریابی کوانتومی عمیق برای مسیریابی درهم تنیده در شبکه های کوانتومی-2022 Quantum routing plays a key role in the development of the next-generation network system. In
particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping
among particles (e.g., photons) associated with nodes in the network. From another side of computing,
machine learning has achieved numerous breakthrough successes in various application domains, including
networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum
networking as in other areas. To bridge this gap, in this article, we propose a novel quantum routing model
for quantum networks that employs machine learning architectures to construct the routing path for the
maximum number of demands (source–destination pairs) within a time window. Specifically, we present a
deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA
utilizes an empirically designed deep neural network that observes the current network states to accommodate
the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training
process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated
requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a
rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in
extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the
model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum
networks.
INDEX TERMS: Deep learning | deep reinforcement learning (DRL) | machine learning | next-generation network | quantum network routing | quantum networks. |
مقاله انگلیسی |
3 |
Moving towards intelligent telemedicine: Computer vision measurement of human movement
حرکت به سمت پزشکی از راه دور هوشمند: اندازه گیری بینایی کامپیوتری حرکت انسان-2022 Background: Telemedicine video consultations are rapidly increasing globally, accelerated by the COVID-
19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual
judgement because video cameras are so ubiquitous in personal devices and new techniques, such as
DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy
of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has
never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations.
Objectives: To determine the validity and reliability of DLC applied to laptop videos to measure finger tapping,
a validated test of human movement.
Method: Sixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand
movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak,
a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101,
ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the
laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking.
Results: Over 96% (529/552) of DLC measures were within +∕−0.5 Hz of the Optotrak measures. At tapping
frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with
the laptop camera’s low FPS. Computer vision methods hold potential for moving us towards intelligent
telemedicine by providing human movement analysis during consultations. However, further developments
are required to accurately measure the fastest movements.
keywords: پزشکی از راه دور | ضربه زدن با انگشت | موتور کنترل | کامپیوتری | Telemedicine | DeepLabCut | Finger tapping | Motor control | Computer vision |
مقاله انگلیسی |
4 |
Efficient Hardware Implementation of Finite Field Arithmetic AB + C for Binary Ring-LWE Based Post-Quantum Cryptography
اجرای سخت افزار کارآمد محاسبات میدان محدود AB + C برای رمزنگاری پس کوانتومی مبتنی بر حلقه باینری-LWE-2022 Post-quantum cryptography (PQC) has gained significant attention from the community
recently as it is proven that the existing public-key cryptosystems are vulnerable to the attacks launched from
the well-developed quantum computers. The finite field arithmetic AB þ C, where A and C are integer polynomials and B is a binary polynomial, is the key component for the binary Ring-learning-with-errors (BRLWE)-
based encryption scheme (a low-complexity PQC suitable for emerging lightweight applications). In this paper,
we propose a novel hardware implementation of the finite field arithmetic AB þ C through three stages of interdependent efforts: (i) a rigorous mathematical formulation process is presented first; (ii) an efficient hardware
architecture is then presented with detailed description; (iii) a thorough implementation has also been given
along with the comparison. Overall, (i) the proposed basic structure (u ¼ 1) outperforms the existing designs,
e.g., it involves 55.9% less area-delay product (ADP) than [13] for n ¼ 512; (ii) the proposed design also offers
very efficient performance in time-complexity and can be used in many future applications.
INDEX TERMS: Binary ring-learning-with-errors | finite field arithmetic | FPGA platform | hardware design | post-quantum cryptography |
مقاله انگلیسی |
5 |
Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سختافزار-نرمافزار روی تراشه مبتنی بر شبکههای عصبی عمیق برای کاربرد بینایی ماشین-2022 Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial
intelligence (AI) in vision applications that make these systems intelligent and able to make
decisions close to or similar to humans. In this context, the AI’s integration on embedded
systems poses many challenges, given that its performance depends on data volume and
quality they assimilate to learn and improve. This returns to the energy consumption and
cost constraints of the FPGA-SoC that have limited processing, memory, and communication
capacity. Despite this, the AI algorithm implementation on embedded systems can drastically
reduce energy consumption and processing times, while reducing the costs and risks associated
with data transmission. Therefore, its efficiency and reliability always depend on the designed
prototypes. Within this range, this work proposes two different designs for the Traffic Sign
Recognition (TSR) application based on the convolutional neural network (CNN) model,
followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based
TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s,
there is room for improvement using programmable logic (PL) and processing system (PS) in a
hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers
will be accelerated to provide a hardware accelerator for each layer where direct memory
access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased
hardware cost, and execution time optimization of 2.13 s, but, there was still room for design
optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated
to be a single computation engine where BRAM interface is used. The implementation results
prove that our proposed embedded TSR design achieves the best performances compared to the
first proposed architectures, in terms of execution time of about 0.03 s, computation roof of
about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1 |
مقاله انگلیسی |
6 |
Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs
Image2Triplets: چارچوب استخراج رابطه صریح مبتنی بر بینایی ماشین برای به روز رسانی نمودارهای دانش فعالیت های ساخت-2022 Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture,
engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However,
research on KG updates in the industry is scarce, with most current research focusing on text-based KG
updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the
potential of computer vision technology for explicit relationship extraction in KG updates is yet to be ex-
plored. This paper combines zero-shot human-object interaction detection techniques with general KGs to
propose a novel framework called Image2Triplets that can extract explicit visual relationships from images
to update the construction activity KG. Comprehensive experiments on the images of architectural dec-
oration processes have been performed to validate the proposed framework. The results and insights will
contribute new knowledge and evidence to human-object interaction detection, KG update and construc-
tion informatics from the theoretical perspective.
© 2022 Elsevier B.V. All rights reserved. keywords: یادگیری شات صفر | تشخیص تعامل انسان و شی | بینایی ماشین| استخراج رابطه صریح | نمودار دانش | Zero-shot learning | Human-object interaction detection | Computer vision | Explicit relationship extraction | Knowledge graph |
مقاله انگلیسی |
7 |
Epsilon-Nets, Unitary Designs, and Random Quantum Circuits
شبکه های اپسیلون، طرح های واحد و مدارهای کوانتومی تصادفی-2022 Epsilon-nets and approximate unitary t-designs are
natural notions that capture properties of unitary operations
relevant for numerous applications in quantum information
and quantum computing. In this work we study quantitative
connections between these two notions. Specifically, we prove
that, for d dimensional Hilbert space, unitaries constituting
δ-approximate t-expanders form -nets for t d5/2 and δ
3d/2 d2. We also show that for arbitrary t, -nets can be used
to construct δ-approximate unitary t-designs for δ t, where
the notion of approximation is based on the diamond norm.
Finally, we prove that the degree of an exact unitary t design
necessary to obtain an -net must grow at least as fast as 1 (for
fixed dimension) and not slower than d2 (for fixed ). This shows
near optimality of our result connecting t-designs and nets.
We apply our findings in the context of quantum computing.
First, we show that that approximate t-designs can be generated
by shallow random circuits formed from a set of universal twoqudit gates in the parallel and sequential local architectures
considered in (Brandão et al., 2016). Importantly, our gate sets
need not to be symmetric (i.e., contains gates together with
their inverses) or consist of gates with algebraic entries. Second,
we consider compilation of quantum gates and prove a nonconstructive Solovay-Kitaev theorem for general universal gate
sets. Our main technical contribution is a new construction of
efficient polynomial approximations to the Dirac delta in the
space of quantum channels, which can be of independent interest.]
Index Terms: Unitary designs, epsilon nets | random quantum circuits | compilation of quantum gates | unitary channels. |
مقاله انگلیسی |
8 |
Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022 There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant
of the successful delivery of site operations. Although manufacturers provide equipment performance hand-
books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis
of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance
monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment
with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated
image libraries are used to train and test several backbone architectures. Experimental results reveal the pre-
cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel
shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the
ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of
potentials to influence current practice of articulated machinery monitoring in projects. keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures |
مقاله انگلیسی |
9 |
Mapping Nearest Neighbor Compliant Quantum Circuits Onto a 2-D Hexagonal Architecture
نگاشت مدارهای کوانتومی منطبق با نزدیکترین همسایه بر روی یک معماری دو بعدی شش ضلعی-2022 Quantum algorithms can be described as quantum
circuits and are supposed to be carried out on an ideal quantum device that is far from current ones. The current quantum
devices have a significant limitation on the connectivity between
quantum bits. In other words, a quantum bit is only allowed
to interact with its nearest neighbors (NNs). In reality, quantum bits have to be placed on a grid, where the connectivity
between quantum bits is predefined. The predefined connectivity
of a grid further determines the possible range of architectures
of a quantum device after the placement of quantum bits. In
this article, we propose to place quantum bits based on a 2-D
hexagonal architecture rather than a 2-D Cartesian architecture. To validate the effectiveness, we leverage a workflow for
mapping NN compliant quantum circuits onto targeting grids,
where the workflow consists of a global reordering strategy and
a local reordering strategy. With the advantages of the hexagonal
grid, the overhead of making quantum circuits NN compliant is
reduced significantly compared with the Cartesian grid. We also
provide a comprehensive set of ablation analyses to gain a better
understanding of the contributions of the components within our
workflow. According to the experimental results, when changing
the grid type from Cartesian to hexagonal, the global reordering strategy is crucial for small quantum circuits. In contrast,
the local reordering strategy is more important than the global
reordering strategy for large quantum circuits.
Index Terms: 2-D architecture | hexagonal grid | nearest neighbor (NN) compliant | quantum circuit. |
مقاله انگلیسی |
10 |
GAFL: Global adaptive filtering layer for computer vision
GAFL: لایه فیلتر تطبیقی جهانی برای بینایی کامپیوتر-2022 We devise a universal global adaptive filtering layer, GAFL, capable of ‘‘learning’’ optimal frequency filter for
each image in a dataset together with the weights of the base neural network that performs some computer
vision task. The proposed approach takes the source image in the spatial domain, selects the best frequencies
in the Fourier domain for the benefit of the global task, and prepends the inverse-transform image to the
main neural network for a joint training. Remarkably, such a simple add-on layer, capable of optimizing the
frequency content of an input for a specific task, dramatically improves the performance of the main network
regardless of its design. We observe that the light networks gain a noticeable boost in the performance metrics;
whereas, the training of the heavy ones converges faster when GAFL is prepended to the main architecture.
We showcase the performance of the layer in four classical computer vision tasks: classification, segmentation,
denoising, and erasing, considering popular natural and medical data benchmarks.
keywords: Adaptive neural layer | Efficient training | Fourier filtering |
مقاله انگلیسی |